TY - GEN
T1 - Human-Machine Interface Evaluation Using EEG in Driving Simulator
AU - Liu, Yuan Cheng
AU - Figalova, Nikol
AU - Baumann, Martin
AU - Bengler, Klaus
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Automated vehicles are pictured as the future of transportation, and facilitating safer driving is only one of the many benefits. However, due to the constantly changing role of the human driver, users are easily confused and have little knowledge about their responsibilities. Being the bridge between automation and human, the human-machine interface (HMI) is of great importance to driving safety. This study was conducted in a static driving simulator. Three HMI designs were developed, among which significant differences in mental workload using NASA-TLX and the subjective transparency test were found. An electroencephalogram was applied throughout the study to determine if differences in the mental workload could also be found using EEG's spectral power analysis. Results suggested that more studies are required to determine the effectiveness of the spectral power of EEG on mental workload, but the three interface designs developed in this study could serve as a solid basis for future research to evaluate the effectiveness of psychophysiological measures.
AB - Automated vehicles are pictured as the future of transportation, and facilitating safer driving is only one of the many benefits. However, due to the constantly changing role of the human driver, users are easily confused and have little knowledge about their responsibilities. Being the bridge between automation and human, the human-machine interface (HMI) is of great importance to driving safety. This study was conducted in a static driving simulator. Three HMI designs were developed, among which significant differences in mental workload using NASA-TLX and the subjective transparency test were found. An electroencephalogram was applied throughout the study to determine if differences in the mental workload could also be found using EEG's spectral power analysis. Results suggested that more studies are required to determine the effectiveness of the spectral power of EEG on mental workload, but the three interface designs developed in this study could serve as a solid basis for future research to evaluate the effectiveness of psychophysiological measures.
UR - http://www.scopus.com/inward/record.url?scp=85167971667&partnerID=8YFLogxK
U2 - 10.1109/IV55152.2023.10186567
DO - 10.1109/IV55152.2023.10186567
M3 - Conference contribution
AN - SCOPUS:85167971667
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
BT - IV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE Intelligent Vehicles Symposium, IV 2023
Y2 - 4 June 2023 through 7 June 2023
ER -